A Domain Adaptation-based Detector for Cooperative Spectrum Sensing

Emerging machine learning approaches provide effective solutions in a data-driven manner for cooperative spectrum sensing (CSS). Their success relies on large amounts of labeled training data to capture spectrum characteristics. However, data collection and annotation under dynamic spectrum environments are time-consuming and impractical. To this end, we propose a novel Domain Adaptation-based Detector for CSS (DADCSS) to improve the sensing adaptiveness and robustness under dynamic environments. It learns environment-invariant features, reduces the domain shift via the inter-domain and intraclass feature alignments, and determines the channel status by an adaptive detector trained by the aligned features. Simulation results demonstrate the effectiveness of our proposed method.